1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
22/2/2023 Uber 6414 Tami NA
22/2/2023 Comida 52690 Tami Restaurant Valpo
22/2/2023 Uber 5215 Tami NA
24/2/2023 Uber 8458 Tami NA
24/2/2023 Comida 7300 Tami Helados Reñaca
25/2/2023 Uber 2889 Tami NA
26/2/2023 Uber 6876 Tami NA
26/2/2023 Enceres 7500 Andrés Merval
26/2/2023 Comida 68970 Andrés Il papparazzo
26/2/2023 Electricidad 40440 Andrés Enel
26/2/2023 Comida 18480 Tami Café Turri Valpo
26/2/2023 Uber 9602 Tami NA
27/2/2023 Comida 9090 Tami Copec
27/2/2023 Bencina + tag 40000 Tami NA
27/2/2023 Comida 62535 Tami NA
1/3/2023 Uber 2960 Andrés uber jueves reñaca-viña tarde
1/3/2023 Uber 3433 Andrés uber martes tarde reñaca
1/3/2023 Uber 3308 Andrés viaje lunes noche
1/3/2023 Comida 7200 Andrés Frutos secos
5/3/2023 Comida 62296 Tami NA
6/3/2023 Enceres 110000 Andrés arreglo reja
9/3/2023 Forro cortina ducha 2490 Tami NA
4/3/2023 Microondas regalo 40000 Tami NA
9/3/2023 Comida 106490 Tami Soul Bar
9/3/2023 Comida 27642 Tami NA
13/3/2023 Comida 51473 Tami NA
13/3/2023 Diosi 20990 Tami Antiparasitario
16/3/2023 Vacunas Influenza 19980 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 5.8551e+08   2    6.1203 0.0023 ** 
## lag_depvar    8.2562e+10   1 1726.0362 <2e-16 ***
## Residuals     2.6691e+10 558                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr    p adj
## 1-0  7228.838   985.8502 13471.83 0.018385
## 2-0 27878.558 22165.4963 33591.62 0.000000
## 2-1 20649.720 17236.1307 24063.31 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
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## 35   33634.57             0   32021.43
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## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   406 50112.82 15540.714
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2000.614782   4032.542491   -530.265070   2444.839364  -2954.194259 
##             7             8             9            10            11 
##    524.357145  -5647.598365  -1197.012269  -3976.297969   -437.821547 
##            12            13            14            15            16 
##  -4956.724478  -1638.336675   -928.476355    351.205345  -3262.917503 
##            17            18            19            20            21 
##   -404.051812  -2152.449918   6579.521625  -1528.129424  -1210.590497 
##            22            23            24            25            26 
##   1471.407162  -1183.937122    234.852722   1697.599524  -7092.694883 
##            27            28            29            30            31 
##    934.886545   8186.142189    440.672868      8.779585  -2378.928186 
##            32            33            34            35            36 
##   1589.270220   4590.745479   1159.243753   2425.110690  -1829.130706 
##            37            38            39            40            41 
##   4637.765548   4321.355849  -2246.144896  -2962.676174  -1103.081567 
##            42            43            44            45            46 
## -10738.658010   7257.727615   2552.949656   1371.363008   8113.609752 
##            47            48            49            50            51 
##    719.499624   6560.248721   6762.871739  -5818.416366  -4758.137843 
##            52            53            54            55            56 
##  -5042.228507  -7929.214302   6104.166517  -4079.369987  -4909.575396 
##            57            58            59            60            61 
##   3827.032976    875.078556    -40.313945    135.084293  -5002.092219 
##            62            63            64            65            66 
##  18106.065610   3680.394702  -3599.623871   5954.584340   7388.668978 
##            67            68            69            70            71 
##  14701.528297   1794.852221 -13118.190307  -1265.227759   4675.395030 
##            72            73            74            75            76 
##  -4857.403939  -4382.357958 -10491.754008   2438.976945  -5415.931528 
##            77            78            79            80            81 
##   1032.908100  -6889.555366    505.204599  -2389.001233  -2731.078542 
##            82            83            84            85            86 
##  -3972.585968   -585.176987   2271.556383   3732.552605    462.555212 
##            87            88            89            90            91 
##   -495.475293    185.648209   4293.092786  -1157.084803   1152.503538 
##            92            93            94            95            96 
##  -2059.265751  -1046.093286    172.875830    271.376047  -7486.004972 
##            97            98            99           100           101 
##   2366.768598  -8616.598146  -2979.513253  -4082.280465  -1786.332469 
##           102           103           104           105           106 
##  -1309.670646   3135.181183  -2371.830221   2560.920521  -1179.009488 
##           107           108           109           110           111 
##    949.040533   2571.602128  -3159.663099  -4737.328595   -877.266215 
##           112           113           114           115           116 
##   1877.507337  11676.976575  -1220.353162   2684.258290   4285.136178 
##           117           118           119           120           121 
##   3535.722403  -1059.867265  -4684.501778  -3710.771179   2320.036081 
##           122           123           124           125           126 
##  -1724.907726   1342.027602   8864.088445    880.157677    162.227805 
##           127           128           129           130           131 
##  -2492.844420   2672.260775   7076.092310   1055.337676  -8458.521129 
##           132           133           134           135           136 
##   1758.550530   4149.362535  -3138.711126  -1407.364722   -847.398338 
##           137           138           139           140           141 
##  -3876.711614   1174.019840   -499.445620  -2918.406967   1705.073023 
##           142           143           144           145           146 
##  -1886.945491  -7840.107562   2005.752560  -3502.609041   2071.512073 
##           147           148           149           150           151 
##   -277.613821   1004.754561   -371.947358   1340.150014   1180.460070 
##           152           153           154           155           156 
##   3355.021346  -4852.528655  -1181.407213  -3245.375639   5938.420255 
##           157           158           159           160           161 
##   9749.406543  -3282.815297  -4635.006114   3738.187367    346.957014 
##           162           163           164           165           166 
##   2853.337530  -5741.545780  -6594.481047   4293.226788  17547.518102 
##           167           168           169           170           171 
##   3831.594384   -188.638257  -2241.699823   -910.335305   3778.833218 
##           172           173           174           175           176 
##    -31.936906  -7882.694383   3031.808767   4501.940579    813.138188 
##           177           178           179           180           181 
##   8937.638693  -9039.872546  -3292.904255 -10576.177480 -11100.425351 
##           182           183           184           185           186 
##   1348.210139   9417.283783  -1275.437129   6081.031872   6724.415531 
##           187           188           189           190           191 
##  13342.038898   8642.198169  -3841.018664   2664.835362  10565.613927 
##           192           193           194           195           196 
##  -1430.561924  -2246.473314 -10096.984141  -6210.314219   1370.199666 
##           197           198           199           200           201 
##  -5092.234868  -9666.104297   5493.717327  -2938.885030  -1586.584672 
##           202           203           204           205           206 
##   -678.179632   6622.347987  10026.554976    742.790005   3086.385816 
##           207           208           209           210           211 
##   3262.723953   5952.274179  13010.229856  -5487.928527 -11117.264508 
##           212           213           214           215           216 
##  -5516.885185 -10450.275777  -4959.023489   1637.458638 -12890.027417 
##           217           218           219           220           221 
##  16486.716159   7949.739011   1685.641783  26846.872364  12736.607909 
##           222           223           224           225           226 
##   7559.587007  14252.999230  -3671.902100  -1522.946013   3980.161025 
##           227           228           229           230           231 
##    561.985234   2941.051731   9199.349129   6039.752749  -1689.853806 
##           232           233           234           235           236 
##  -1623.619973   9619.425216 -11300.046289  -7108.114607  -8385.304683 
##           237           238           239           240           241 
##  -9965.062461   3194.126994   1480.404771  -8162.336089  -8869.885718 
##           242           243           244           245           246 
##   9200.161447  -7633.923960   2605.394678 -10172.970287  -3943.928123 
##           247           248           249           250           251 
##   1530.204043   1119.612254 -12192.629081   3744.922600   2178.126250 
##           252           253           254           255           256 
##   4335.882562   2270.051198  -1020.105406  11277.348096  21039.996937 
##           257           258           259           260           261 
##   3389.110849  -4083.982868   4279.453232  -1523.607346   3898.496258 
##           262           263           264           265           266 
##  -4688.562355 -10744.521488  -4602.288328   -403.106145  -5068.534254 
##           267           268           269           270           271 
##   8890.112092  -4151.109733   4310.306179  -1978.675296   4554.505287 
##           272           273           274           275           276 
##    839.488221   7432.738662  -1272.879239  12157.896822  -4437.418775 
##           277           278           279           280           281 
##   1858.073934   -241.118945   7978.337054  -4922.353648  -2607.054540 
##           282           283           284           285           286 
## -11142.021266  -2564.253438  18759.831306   7915.152108   2872.104985 
##           287           288           289           290           291 
##   -492.136832   1036.995809   6526.604979   7015.045113 -18635.945823 
##           292           293           294           295           296 
## -11025.905903  -8016.471528   9767.787165   3191.608705  -1053.761027 
##           297           298           299           300           301 
##  27527.497529  10217.206268   5055.785637   9670.339300   3011.019505 
##           302           303           304           305           306 
##   -882.519210   8039.336234 -24150.009419  -3411.963559    -52.035500 
##           307           308           309           310           311 
##  -6841.441451  -3846.597175   3059.576892  -9057.557494  -3097.267460 
##           312           313           314           315           316 
##  -8049.162161   1703.588212  -3005.578125   2196.371028  -3929.705984 
##           317           318           319           320           321 
##  27597.624231   -574.691612   3435.572561  10973.070614   5734.253786 
##           322           323           324           325           326 
##  32524.530982   5269.975661 -20781.051931   1932.708910   1250.456893 
##           327           328           329           330           331 
##  -6325.439210  -1598.049191 -33130.931761   1042.653307  -2129.582119 
##           332           333           334           335           336 
##     88.057027  -2978.784945   4279.917668   -236.760380  -6749.098801 
##           337           338           339           340           341 
##  -2911.650420  -1983.662627  -7468.482286   4064.390738  -1155.371508 
##           342           343           344           345           346 
##  -1520.731697   -775.756412    395.755825    701.432254  -1398.837857 
##           347           348           349           350           351 
##  -9227.888124 -12993.228076   2526.135218  -4104.775332  -3438.822033 
##           352           353           354           355           356 
##  -5758.796973   1973.051499   1607.292978   2975.084347  -3546.971517 
##           357           358           359           360           361 
##   -299.218494    892.642710   7226.938597    486.317781    168.399365 
##           362           363           364           365           366 
##   2787.111542  -2548.807246   -676.367036  -8542.169132  -4420.562529 
##           367           368           369           370           371 
##  -6002.987162  -4735.386635  -7034.235404   5237.864691    591.927104 
##           372           373           374           375           376 
##   7339.493539  -7421.111806  -2045.423863  -3168.837991  -2245.807935 
##           377           378           379           380           381 
## -12234.405595   2131.619522 -10405.520669   5929.115011   9565.643640 
##           382           383           384           385           386 
##   3348.095890  -2182.207743   1818.748429   6954.175270  11613.025722 
##           387           388           389           390           391 
##  -5612.716136  -5178.879950     24.175767   8742.440450   1986.224588 
##           392           393           394           395           396 
##  11388.135842  -9723.522600   2928.783317    863.465307    711.056452 
##           397           398           399           400           401 
##   -506.640414   -416.216973 -14340.130828   8686.418159  -1015.383068 
##           402           403           404           405           406 
##  -1202.507871   7155.876700  -7761.590514  -1117.064165  -2346.044233 
##           407           408           409           410           411 
##  -5626.770814  -2656.732688  -3707.697301  -8538.760066   6360.337222 
##           412           413           414           415           416 
##   1863.294509  -7152.474950  -7465.070322  14455.338884   4033.732627 
##           417           418           419           420           421 
##   4699.650878  -7837.923534  -4540.688851  -2391.721748   3033.690787 
##           422           423           424           425           426 
## -13798.633488  -2565.564997  -8868.083559   3253.524650   7215.220976 
##           427           428           429           430           431 
##   6804.804076  -3767.698150  -3899.418220  -4497.673461  -1560.888836 
##           432           433           434           435           436 
##  -5481.584979  -6391.603402  -5709.015730  -1147.792060   -601.600089 
##           437           438           439           440           441 
##  -4729.234348   2830.355999   5084.060688  -4819.089063  -1918.606113 
##           442           443           444           445           446 
##   1816.153326  -3600.469693   3073.712594  -6344.081419 -11872.619504 
##           447           448           449           450           451 
##  -4264.977558   9895.055460  -1789.442266   4997.410427  -5631.089433 
##           452           453           454           455           456 
##   -881.147941    625.475171   3266.815546 -12031.142355   3613.054839 
##           457           458           459           460           461 
##  -6459.877407   6766.974903   3252.269081   2743.687720  -3612.344631 
##           462           463           464           465           466 
##   2327.186029    224.733513   2023.855787   -292.641971   3578.121945 
##           467           468           469           470           471 
##  -2414.632491   6030.968111  -6719.786438  -2736.639230  -1973.907832 
##           472           473           474           475           476 
##  -4429.082758   3235.588057   8035.675021  -5783.393116   1721.386161 
##           477           478           479           480           481 
##  -5942.024771  -2603.526152   2255.345288 -12686.957495  -9506.304906 
##           482           483           484           485           486 
##   -948.790184    275.102929   -707.088457  -1086.501137  -9329.221632 
##           487           488           489           490           491 
##  11353.881635   6488.958185   7669.701487  -5192.659977   5611.527216 
##           492           493           494           495           496 
##   9533.193265   6287.628740 -13245.100027 -10332.538358  -3202.038230 
##           497           498           499           500           501 
##   -863.436515   -279.872886  -7380.029319    861.599314   4539.613128 
##           502           503           504           505           506 
##   5759.633013    908.926682    328.134873  -6992.068016    819.516402 
##           507           508           509           510           511 
##  -4798.531378   2084.511183  -1044.692929  -7905.615087   -346.979984 
##           512           513           514           515           516 
##  -2418.668614   -330.786050   1589.470777  -9238.536671  -7506.109615 
##           517           518           519           520           521 
##  24547.869995  10078.246883   6125.986519  -5094.633625   3038.156873 
##           522           523           524           525           526 
##  17255.749480  11701.209002 -23929.481698  -4834.027503  -3505.073316 
##           527           528           529           530           531 
##   4802.818921   -127.029995 -10872.687138   4626.308713  14144.331660 
##           532           533           534           535           536 
##  -4747.322731   4603.510815   5782.091537  -1566.818264  -4317.742449 
##           537           538           539           540           541 
##  -6848.992913  -1871.308395   8554.701151    357.762785  -7910.615086 
##           542           543           544           545           546 
##   2050.640570   -364.159642    602.893355 -10793.530449 -10819.338439 
##           547           548           549           550           551 
##   2288.690349   7252.764559  -1069.750117   1085.477342  -7472.259908 
##           552           553           554           555           556 
##   8816.205660   1155.251538 -11696.853010   9412.856404   8904.211383 
##           557           558           559           560           561 
##    340.119269   5091.673204  -3339.845495  14342.227139  21724.886527 
##           562           563 
##  -6252.728827  -9468.279834 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17268.67 20106.46 24346.41 24065.30 26410.91 23752.36 24466.31 19714.16 
##       10       11       12       13       14       15       16       17 
## 19451.58 16803.11 17578.01 14318.19 14369.19 15031.65 16722.63 15048.19 
##       18       19       20       21       22       23       24       25 
## 16079.45 15455.05 22514.13 21601.16 21082.74 22966.51 22294.72 22945.11 
##       26       27       28       29       30       31       32       33 
## 24784.98 18733.40 20453.86 28265.33 28322.79 27996.79 25634.02 27031.83 
##       34       35       36       37       38       39       40       41 
## 30862.18 31209.46 32613.99 30132.81 34121.64 37319.14 34384.96 31206.37 
##       42       43       44       45       46       47       48       49 
## 30057.94 20668.56 28162.48 30590.92 31676.53 38492.07 37988.32 42635.13 
##       50       51       52       53       54       55       56       57 
## 46857.42 39579.42 34165.80 29204.93 22371.98 28641.23 25233.15 21542.97 
##       58       59       60       61       62       63       64       65 
## 25936.78 27192.17 27488.20 27898.66 23783.22 40319.75 42157.62 37419.27 
##       66       67       68       69       70       71       72       73 
## 41612.33 46511.76 57144.72 55165.05 40456.94 37971.03 40978.98 35297.93 
##       74       75       76       77       78       79       80       81 
## 30765.18 21499.31 24690.22 20629.38 22708.56 17620.94 19629.72 18858.79 
##       82       83       84       85       86       87       88       89 
## 17889.73 15965.03 17238.59 20834.73 25237.87 26224.48 26249.35 26864.05 
##       90       91       92       93       94       95       96       97 
## 30975.51 29809.93 30805.98 28876.81 28079.27 28446.20 28851.43 22450.09 
##       98       99      100      101      102      103      104      105 
## 25455.17 18508.66 17368.57 15415.76 15714.53 16389.68 20847.54 19934.08 
##      106      107      108      109      110      111      112      113 
## 23433.58 23224.25 24894.83 27762.09 25268.47 21723.69 21998.21 24635.74 
##      114      115      116      117      118      119      120      121 
## 35464.35 33663.17 35494.58 38482.99 40432.44 38128.50 32966.63 29320.11 
##      122      123      124      125      126      127      128      129 
## 31396.05 29681.69 30859.34 38433.99 38077.63 37142.27 34016.17 35791.48 
##      130      131      132      133      134      135      136      137 
## 41171.52 40613.66 31844.45 33105.07 36284.28 32706.79 31099.40 30187.43 
##      138      139      140      141      142      143      144      145 
## 26755.84 28165.59 27935.98 25629.93 27647.66 26276.96 19900.25 22920.75 
##      146      147      148      149      150      151      152      153 
## 20754.63 23721.90 24260.10 25845.23 26026.71 27675.40 28971.84 31993.96 
##      154      155      156      157      158      159      160      161 
## 27479.12 26744.52 24307.87 30182.45 41303.24 39639.01 37012.67 42016.33 
##      162      163      164      165      166      167      168      169 
## 43420.23 46824.83 42305.77 37628.49 43035.77 59283.98 61488.78 59908.13 
##      170      171      172      173      174      175      176      177 
## 56744.34 55148.88 57842.51 56869.84 49187.48 52001.63 55731.86 55767.93 
##      178      179      180      181      182      183      184      185 
## 62873.16 53406.90 50168.61 41007.71 32575.08 36071.72 46141.72 45599.54 
##      186      187      188      189      190      191      192      193 
## 51532.58 57258.53 68005.80 73271.16 66986.74 67179.53 74226.42 69917.19 
##      194      195      196      197      198      199      200      201 
## 65454.84 54734.31 48784.23 50203.81 45813.10 38007.85 44411.31 42644.58 
##      202      203      204      205      206      207      208      209 
## 42283.75 42760.51 49532.02 58391.78 58022.61 59741.70 61392.01 65170.63 
##      210      211      212      213      214      215      216      217 
## 74605.79 66714.84 54943.03 49569.70 40595.88 37563.68 40667.03 30720.28 
##      218      219      220      221      222      223      224      225 
## 47637.55 54934.07 55832.98 78522.96 85993.13 87989.72 95555.90 86536.80 
##      226      227      228      229      230      231      232      233 
## 80555.12 80138.44 76799.52 75963.79 80685.10 82044.85 76498.76 71727.57 
##      234      235      236      237      238      239      240      241 
## 77362.47 64054.54 56117.45 48094.78 39734.16 43912.17 46057.76 39530.17 
##      242      243      244      245      246      247      248      249 
## 33230.70 43479.07 37745.03 41667.68 33957.21 32667.37 36310.53 39125.06 
##      250      251      252      253      254      255      256      257 
## 29984.93 35903.30 39692.12 44869.66 47578.96 47073.22 57340.00 74779.17 
##      258      259      260      261      262      263      264      265 
## 74594.84 67927.69 69404.61 65637.93 67079.28 60857.66 50167.86 46208.39 
##      266      267      268      269      270      271      272      273 
## 46417.11 42536.75 51311.68 47597.12 51730.10 49852.92 53906.80 54201.83 
##      274      275      276      277      278      279      280      281 
## 60199.31 57841.39 67482.28 61427.21 61636.55 59991.09 65714.93 59466.20 
##      282      283      284      285      286      287      288      289 
## 56041.45 45628.40 44030.45 61205.56 66717.32 67125.42 64551.58 63641.97 
##      290      291      292      293      294      295      296      297 
## 67629.67 71526.95 52586.48 42721.33 36752.21 47039.39 50270.48 49387.36 
##      298      299      300      301      302      303      304      305 
## 73503.51 79429.21 80094.66 84691.84 82896.38 77943.09 81398.44 56380.39 
##      306      307      308      309      310      311      312      313 
## 52653.89 52334.73 46145.45 43364.14 46955.56 39532.41 38258.73 32838.27 
##      314      315      316      317      318      319      320      321 
## 36610.29 35794.34 39613.13 37604.23 63305.26 61153.57 62771.79 70743.46 
##      322      323      324      325      326      327      328      329 
## 73122.90 98520.31 96903.34 72813.43 71615.26 69978.01 61956.33 59088.07 
##      330      331      332      333      334      335      336      337 
## 29135.78 32811.15 33249.23 35561.50 34904.51 40652.47 41724.53 36987.79 
##      338      339      340      341      342      343      344      345 
## 36204.81 36331.05 31665.47 37644.66 38305.87 38563.47 39436.39 41216.42 
##      346      347      348      349      350      351      352      353 
## 43032.41 42784.89 35752.80 26351.72 31678.78 30543.54 30134.94 27759.23 
##      354      355      356      357      358      359      360      361 
## 32422.71 36164.63 40613.54 38808.50 40064.64 42196.06 49566.97 50115.74 
##      362      363      364      365      366      367      368      369 
## 50316.75 52771.81 50263.51 49709.88 42379.28 39585.27 35774.82 33560.81 
##      370      371      372      373      374      375      376      377 
## 29631.56 36895.50 39174.94 47034.54 41026.00 40474.98 39017.09 38551.41 
##      378      379      380      381      382      383      384      385 
## 29449.09 34032.09 27106.60 35298.93 45598.05 49151.78 47430.82 49415.97 
##      386      387      388      389      390      391      392      393 
## 55615.69 65070.00 58303.59 52789.97 52519.56 59874.92 60396.58 69036.81 
##      394      395      396      397      398      399      400      401 
## 58178.22 59739.96 59301.51 58787.07 57278.93 56044.56 42846.58 51404.10 
##      402      403      404      405      406      407      408      409 
## 50407.79 49377.41 55757.73 48324.64 47638.04 45970.20 41661.59 40496.13 
##      410      411      412      413      414      415      416      417 
## 38566.33 32679.81 40526.85 43443.62 38133.36 33237.66 48060.70 51892.92 
##      418      419      420      421      422      423      424      425 
## 55809.35 48303.12 44638.44 43318.74 46893.49 35350.42 35080.51 29358.05 
##      426      427      428      429      430      431      432      433 
## 34929.64 43230.05 50099.70 46875.70 43953.96 40889.17 40777.73 37267.03 
##      434      435      436      437      438      439      440      441 
## 33418.02 30661.08 32232.03 34075.38 32086.50 36936.80 43122.09 39885.03 
##      442      443      444      445      446      447      448      449 
## 39591.99 42588.61 40481.57 44458.08 39720.48 30781.98 29623.23 40943.16 
##      450      451      452      453      454      455      456      457 
## 40625.73 46258.52 41908.86 42257.38 43872.61 47578.71 37485.95 42319.45 
##      458      459      460      461      462      463      464      465 
## 37757.60 45302.02 48810.60 51422.63 48162.81 50495.98 50696.86 52438.21 
##      466      467      468      469      470      471      472      473 
## 51937.45 54871.63 52208.60 57243.36 50525.21 48143.91 46734.65 43369.98 
##      474      475      476      477      478      479      480      481 
## 47113.90 54552.96 48998.04 50695.74 45501.53 43885.80 46709.53 36158.16 
##      482      483      484      485      486      487      488      489 
## 29740.65 31603.90 34291.80 35776.93 36739.65 30401.12 42890.61 49529.16 
##      490      491      492      493      494      495      496      497 
## 56337.23 51065.90 55883.24 63492.09 67291.10 53592.11 44200.61 42232.01 
##      498      499      500      501      502      503      504      505 
## 42554.16 43342.74 37847.40 40238.53 45522.80 51185.93 51893.29 52003.50 
##      506      507      508      509      510      511      512      513 
## 45725.91 47061.53 43332.92 46079.41 45746.19 39482.41 40609.81 39787.64 
##      514      515      516      517      518      519      520      521 
## 40889.67 43521.11 36384.54 31679.27 55491.18 63625.30 67266.35 60666.99 
##      522      523      524      525      526      527      528      529 
## 62002.11 75543.51 82497.48 57529.31 52416.07 49121.18 53485.89 52993.83 
##      530      531      532      533      534      535      536      537 
## 43209.41 48184.95 60804.18 55342.92 58729.48 62704.25 59766.46 54813.42 
##      538      539      540      541      542      543      544      545 
## 48297.02 46957.30 54868.52 54619.76 47204.07 49420.45 49247.68 49939.24 
##      546      547      548      549      550      551      552      553 
## 40618.77 32481.17 36808.81 44898.89 44696.52 46396.83 40426.22 49409.75 
##      554      555      556      557      558      559      560      561 
## 50561.28 40373.86 49883.65 57720.74 57087.76 60673.70 56454.77 68176.83 
##      562      563 
## 84810.87 74934.28 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8392
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    6.120318  0.5483265     3.23998
## t2* 1726.036188 26.0080576   230.70820
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    2.106346       6.238509   12.66864
## 2    lag_depvar 1401.335790    1734.037187 2161.65413

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Mar 20 00:43:42 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 20 00:43:52 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 20 00:44:02 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 20 00:44:12 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 20 00:44:22 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 20 00:44:32 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 20 00:44:42 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 20 00:44:52 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Mar 20 00:45:02 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 20 00:45:12 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 0.0000 5.410333 5.629750 6.6938684
Comida 344.5585 310.278417 314.087500 340.5900263
Comunicaciones 0.0000 0.000000 0.000000 0.0000000
Electricidad 20.2200 47.072333 38.297667 31.7589211
Enceres 3.7500 20.086417 17.443792 23.5340789
Farmacia 0.0000 1.831667 7.913875 9.4308947
Gas/Bencina 35.1500 44.325000 28.954333 25.5869474
Diosi 13.7950 31.180667 41.934250 38.9333684
donaciones/regalos 0.0000 0.000000 7.170083 7.2294474
Electrodomésticos/ Mantención casa 0.0000 3.944000 30.269500 21.8281053
VTR 10.9950 25.156667 22.121792 20.5862632
Netflix 4.1600 7.151583 7.090167 7.3013421
Otros 0.0000 3.151083 1.575542 0.9950789
Total 432.6285 499.588167 522.488250 534.4683421
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1925, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-04-09 00:04:58 sería de: 35.440 pesos// Percentil 95% más alto proyectado: 38.819,45

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 35562.07 35561.61
Lo.80 35562.74 35562.43
Point.Forecast 35564.01 35564.00
Hi.80 37322.24 39884.86
Hi.95 38359.03 42352.99


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.2960  1000.211
## s.e.  0.1424    32.701
## 
## sigma^2 = 27313:  log likelihood = -318.82
## AIC=643.65   AICc=644.18   BIC=649.32
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2809   741.3630   8.5540
## s.e.  0.1442   394.4049  12.9803
## 
## sigma^2 = 27673:  log likelihood = -318.61
## AIC=645.22   AICc=646.13   BIC=652.79
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 729.8856 661.0978 710.2632
Lo.80 847.4742 778.4768 793.1745
Point.Forecast 1069.6043 1000.2110 977.1022
Hi.80 1291.7345 1221.9451 1273.8932
Hi.95 1409.3231 1339.3241 1465.9060


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.27             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.13.0         
## [13] forecast_8.21       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.0     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.2     forcats_1.0.0      
## [22] dplyr_1.1.0         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.0        ggplot2_3.4.1       tidyverse_2.0.0    
## [28] sjPlot_2.8.13       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.8.0      httr_1.4.5         
## [34] readxl_1.4.2        zoo_1.8-11          stringr_1.5.0      
## [37] stringi_1.7.12      DataExplorer_0.8.2  data.table_1.14.8  
## [40] reshape2_1.4.4      fUnitRoots_4021.80  plyr_1.8.8         
## [43] readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       igraph_1.4.1        lazyeval_0.2.2     
##   [7] splines_4.1.2       crosstalk_1.2.0     digest_0.6.31      
##  [10] htmltools_0.5.4     fansi_1.0.4         ggfortify_0.4.15   
##  [13] magrittr_2.0.3      tzdb_0.3.0          modelr_0.1.10      
##  [16] vroom_1.6.1         timechange_0.2.0    anytime_0.3.9      
##  [19] tseries_0.10-53     colorspace_2.1-0    xfun_0.37          
##  [22] crayon_1.5.2        jsonlite_1.8.4      lme4_1.1-32        
##  [25] glue_1.6.2          r2d3_0.2.6          gtable_0.3.2       
##  [28] emmeans_1.8.5       sjstats_0.18.2      sjmisc_2.8.9       
##  [31] car_3.1-1           quantmod_0.4.20     abind_1.4-5        
##  [34] mvtnorm_1.1-3       DBI_1.1.3           ggeffects_1.2.0    
##  [37] Rcpp_1.0.10         viridisLite_0.4.1   xtable_1.8-4       
##  [40] performance_0.10.2  bit_4.0.5           datawizard_0.6.5   
##  [43] htmlwidgets_1.6.1   timeSeries_4021.105 gplots_3.1.3       
##  [46] ellipsis_0.3.2      spatial_7.3-14      farver_2.1.1       
##  [49] pkgconfig_2.0.3     nnet_7.3-16         sass_0.4.5         
##  [52] dbplyr_2.3.1        janitor_2.2.0       utf8_1.2.3         
##  [55] labeling_0.4.2      tidyselect_1.2.0    rlang_1.1.0        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.7        cli_3.6.0           generics_0.1.3     
##  [64] sjlabelled_1.2.0    broom_1.0.4         evaluate_0.20      
##  [67] fastmap_1.1.1       yaml_2.3.7          knitr_1.42         
##  [70] bit64_4.0.5         caTools_1.18.2      forge_0.2.0        
##  [73] nlme_3.1-153        slam_0.1-50         xml2_1.3.3         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.14    
##  [79] curl_5.0.0          bslib_0.4.2         highr_0.10         
##  [82] fBasics_4022.94     Matrix_1.5-3        its.analysis_1.6.0 
##  [85] nloptr_2.0.3        urca_1.3-3          vctrs_0.6.0        
##  [88] pillar_1.8.1        lifecycle_1.0.3     networkD3_0.4      
##  [91] lmtest_0.9-40       jquerylib_0.1.4     estimability_1.4.1 
##  [94] bitops_1.0-7        insight_0.19.1      R6_2.5.1           
##  [97] KernSmooth_2.23-20  janeaustenr_1.0.0   codetools_0.2-18   
## [100] gtools_3.9.4        boot_1.3-28         MASS_7.3-54        
## [103] assertthat_0.2.1    rprojroot_2.0.3     withr_2.5.0        
## [106] fracdiff_1.5-2      bayestestR_0.13.0   parallel_4.1.2     
## [109] hms_1.1.2           quadprog_1.5-8      timeDate_4022.108  
## [112] minqa_1.2.5         snakecase_0.11.0    rmarkdown_2.20     
## [115] carData_3.0-5       TTR_0.24.3          base64enc_0.1-3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))